Our Human Brainome project seeks to define the genome-transriptome-proteome- phenome interactions in the cortexes of normally aged human brains and brains affected by neurodegenerative disease. We hypothesize that the current accepted approach for discovering novel genetic risk loci by looking at a single layer of information (genotypes) is lacking power and taking a more systems-wide approach might increase the success of finding novel targets. We intend upon comparing our genotype information (~ 1.8 million single nucleotide polymorphisms) and our expression information (~ 46,000 transcripts) with a novel proteomics dataset generated by running Liquid Chromatography coupled online with high mass accuracy Mass Spectrometry (LC- MS, providing quantification of ~2000-3000 proteins). We will look at both single correlative cis and trans relationships (i.e. DNA change affects downstream regulation of one transcript or protein), as well as perform analyses to understand the networks of relationships occurring both at the transcriptome and proteome level. We are well situated to perform this work. First, we have an extensive collection of frozen human brain samples (n~1500, ~60% late onset Alzheimer's disease samples) for which there is genotype and expression data available. Large sample sizes are needed to obtain sufficient power to accurately assess the human genome as well as overcome some of the noise and other issues inherent to transcriptomics and proteomics sample analysis. These existing genotype and gene expression datasets are essential to success in this grant. Second, to accomplish the proteome analyses we will utilize the accurate mass and time (AMT) tag approach developed at the Pacific Northwest National Laboratories to avoid the sensitivity constraints of conventional approaches and improve the throughput of measurements providing broad proteome coverage. While having the same coverage of the proteome, the AMT tag approach typically reduces by 1-2 orders (e.g. 1 hour vs normally 24 hours) of magnitude the instrument time per sample analysis. Thus, the AMT tag approach is the only reasonable option to provide the sensitivity and measurement throughput essential to this project. Finally, we expect to achieve an additional 10-fold increase in sensitivity using our novel de-noising algorithm that will allow for a more accurate assessment of the complete proteome of the human brain cortex. By developing a more global view of the processes involved in human brain expression we will be able to relate new genetic findings to their downstream neuro-pathobiological relevance. This should aid in the development of novel genetic and molecular biomarkers of neurodegenerative disease. Identifying biomarkers that could further classify pre-clinical subgroups and identify sub-classes of rapid converters would help to significantly reduce the cost of drug trials. These biomarkers will have the added benefit that they are not only molecular, but in addition have mapped genotype profiles, which should be easier to assay than a molecular marker. In this project we seek to define the effects of DNA variation on human cortical expression with an emphasis on the DNA variation that is impinging on proteome expression changes relevant to the pathogenesis of Alzheimer's disease. If we achieve our aims we will know specifically which variant or group of variants are changing protein expression levels. This information will help us to define the downstream significance of DNA risk variation in Alzheimer's disease, which might aid in the discovery of novel biomarkers and therapies for this devastating illness.